##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
##
## 'drc' has been loaded.
## Please cite R and 'drc' if used for a publication,
## for references type 'citation()' and 'citation('drc')'.
## Loading required package: lme4
## Loading required package: Matrix
Load csv files with experimental data and microsome data sets. The beolw may return false but still be OK if rstudio does not have privileges to data directory (e.g., attached drive).
## time parent analyte matrix conc replicate
## 4 15 atrazine atrazine microsomes 0.5514354 1
## 47 0 atrazine atrazine microsomes NA 1
## 61 90 atrazine atrazine microsomes NA 3
## 92 0 atrazine atrazine microsomes 697.4815010 1
## 95 15 atrazine atrazine microsomes 161.9606512 1
## 98 30 atrazine atrazine microsomes 237.9761898 1
## 101 60 atrazine atrazine microsomes 255.9782700 1
## 104 90 atrazine atrazine microsomes 121.3271776 1
## 127 0 atrazine atrazine microsomes NA 1
## 134 31 atrazine atrazine microsomes NA 2
## 169 60 atrazine dea microsomes 0.3080022 4
## 206 15 atrazine dea microsomes NA 1
## 209 30 atrazine dea microsomes NA 1
## 212 60 atrazine dea microsomes NA 1
## 215 90 atrazine dea microsomes NA 1
## 231 90 atrazine dea microsomes NA 2
## 235 0 atrazine dea microsomes NA 3
## 255 30 atrazine dea microsomes NA 2
## 259 60 atrazine dea microsomes NA 3
## 344 0 atrazine dia microsomes 0.0000000 1
## 347 15 atrazine dia microsomes 0.3204253 1
## 350 30 atrazine dia microsomes 0.9284476 1
## 353 60 atrazine dia microsomes 1.5356847 1
## 356 90 atrazine dia microsomes 1.0590508 1
## 359 0 atrazine dia microsomes NA 1
## 365 30 atrazine dia microsomes NA 1
## 366 30 atrazine dia microsomes NA 2
## 370 60 atrazine dia microsomes NA 3
## 372 90 atrazine dia microsomes NA 2
## 379 15 atrazine dia microsomes NA 3
## 386 90 atrazine dia microsomes NA 1
## 478 60 fipronil fipsulf microsomes 0.0414000 2
## 547 15 fipronil fipronil microsomes 0.0000000 2
## 591 15 fipronil fipronil microsomes 55.7278739 1
## 635 0 triadimefon tdla microsomes 2.3211093 3
## 657 60 triadimefon tdla microsomes 0.7265999 1
## 668 15 triadimefon tdla microsomes 3.7310331 3
## 710 0 triadimefon tdla microsomes 5.2664595 3
## 713 15 triadimefon tdla microsomes 5.2998384 3
## 803 15 triadimefon tdlb microsomes 3.3031594 3
## 927 60 triadimefon triadimefon microsomes NA 1
## 936 15 triadimefon triadimefon microsomes NA 1
## 1023 0 triadimefon triadimefon microsomes 229.2656431 1
## microMexp X
## 4 0.7120 NA
## 47 50.0000 NA
## 61 50.0000 NA
## 92 125.0000 NA
## 95 125.0000 NA
## 98 125.0000 NA
## 101 125.0000 NA
## 104 125.0000 NA
## 127 250.0000 NA
## 134 250.0000 NA
## 169 10.0000 NA
## 206 125.0000 NA
## 209 125.0000 NA
## 212 125.0000 NA
## 215 125.0000 NA
## 231 0.7120 NA
## 235 3.1684 NA
## 255 250.0000 NA
## 259 250.0000 NA
## 344 125.0000 NA
## 347 125.0000 NA
## 350 125.0000 NA
## 353 125.0000 NA
## 356 125.0000 NA
## 359 0.7120 NA
## 365 0.7120 NA
## 366 0.7120 NA
## 370 0.7120 NA
## 372 0.7120 NA
## 379 3.1684 NA
## 386 3.1684 NA
## 478 75.0000 NA
## 547 10.0000 NA
## 591 150.0000 NA
## 635 0.6800 NA
## 657 10.0000 NA
## 668 2.8900 NA
## 710 100.0000 NA
## 713 100.0000 NA
## 803 2.8900 NA
## 927 10.0000 NA
## 936 2.8900 NA
## 1023 250.0000 NA
Check out structure of imported data sets.
Set time and replicate fields as factors for later statistical inference.
Check out structure of imported data set for the microsome analysis.
Set time and replicate fields as factors for later statistical inference.
The microsomal experiment has 3-4 replicates for each pesticide. The experiment progressively increases the concentration of the substrate (microMexp) and measures the enzyme velocity. At each substrate concentration the reaction is quenched after a specific period of time (0, 15, 30, 60, 90 mins). This time series data is used to estimate a slope associated with each substrate concentration. Substrate concentration and linear velocity slopes are then fit using Michaelis-Menten kinetics to estimate reaction rates for each pesticide.
We use the dose-response model (drm) package from R for the fitting of the rate constant. We provide it the 2-parameter Michaelis-Menton function (mm2) as an argument. It uses the MM.2 function that is in the drc package and therefore is a drc object that contains a list of class drcMean, containing the mean function, the self starter function, the parameter names and other components such as derivatives and a function for calculating ED values. It is not shifted (??) so uses the fit f(x,(c,d,e)) = c + (d-c)/(1+e/x). Where x is the dose, it is a strictly increasing function so we reverse the sign of the slopes for the parents. e is the dose halfway between c and d, for the 2-parameter model c = 0.
## Michaelis-Menten
## (2 parameters)
## In 'drc': MM.2
The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.
## [1] "Substrates:"
## [1] "here are the rates"
##
## Model fitted: Michaelis-Menten (2 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## d:(Intercept) 2.0317e+04 5.6748e+04 3.5802e-01 0.7309
## e:(Intercept) 3.6081e+03 1.0594e+04 3.4059e-01 0.7434
##
## Residual standard error:
##
## 109.7349 (7 degrees of freedom)
We are interested in the reaction rate, v, which is a function of available substrate (pesticide) and the presence of an enzyme. We get Vmax from the asymptote and K = 0.5*Vmax. Biochemical reactions with a single substrate are typically assumed to follow Michaelis-Menten kinetics even if they deviate from the basic model assumptions.
We are fitting the 2-parameter model.
Used to estimate slopes.
The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.
## [1] "Substrates:"
## [1] "here are the rates"
##
## Model fitted: Michaelis-Menten (2 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## d:(Intercept) 119.0693 24.4160 4.8767 0.0018
## e:(Intercept) 31.9046 25.8547 1.2340 0.2570
##
## Residual standard error:
##
## 20.38128 (7 degrees of freedom)
We are interested in the reaction rate, v, which is a function of available substrate (pesticide) and the presence of an enzyme. We get Vmax from the asymptote and K = 0.5*Vmax. Biochemical reactions with a single substrate are typically assumed to follow Michaelis-Menten kinetics even if they deviate from the basic model assumptions.
We are fitting the 2-parameter model.
Used to estimate slopes.
The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.
## [1] "Substrates:"
## Warning in rlm.default(x, y, weights, method = method, wt.method =
## wt.method, : 'rlm' failed to converge in 20 steps
## [1] "here are the rates"
##
## Model fitted: Michaelis-Menten (2 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## d:(Intercept) 5.3408e+03 1.6487e+04 3.2394e-01 0.7555
## e:(Intercept) 8.5689e+03 2.7010e+04 3.1724e-01 0.7603
##
## Residual standard error:
##
## 16.48602 (7 degrees of freedom)
The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.
## [1] "Substrates:"
## [1] "here are the rates"
##
## Model fitted: Michaelis-Menten (2 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## d:(Intercept) 534.1063 92.3057 5.7863 0.0007
## e:(Intercept) 18.3792 15.3117 1.2003 0.2690
##
## Residual standard error:
##
## 121.6792 (7 degrees of freedom)
The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.
## [1] "Substrates:"
## [1] "here are the rates"
##
## Model fitted: Michaelis-Menten (2 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## d:(Intercept) 94.76996 22.19263 4.27033 0.0037
## e:(Intercept) 5.20492 10.73574 0.48482 0.6426
##
## Residual standard error:
##
## 36.04578 (7 degrees of freedom)
The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.
## [1] "Substrates:"
## [1] "here are the rates"
##
## Model fitted: Michaelis-Menten (2 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## d:(Intercept) 580.84356 233.34931 2.48916 0.0416
## e:(Intercept) 65.06472 70.23579 0.92638 0.3851
##
## Residual standard error:
##
## 140.4322 (7 degrees of freedom)
The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.
## [1] "Substrates:"
## [1] "here are the rates"
##
## Model fitted: Michaelis-Menten (2 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## d:(Intercept) 2.7431e+04 1.1366e+05 2.4133e-01 0.8212
## e:(Intercept) 2.1216e+03 9.6407e+03 2.2007e-01 0.8366
##
## Residual standard error:
##
## 973.0673 (4 degrees of freedom)
The above figure shows a three-dimensional plot of pesticide substrate concentration, observed concentration and experiment time. We need to condition the pesticide data on the substrate concentrations used in the experiment and regress the observed concentrations over the 90 min period in order to estimate the linear slopes for the Michaelis-Menten kinetic rate constants.
## [1] "Substrates:"
## [1] "here are the rates"
##
## Model fitted: Michaelis-Menten (2 parms)
##
## Parameter estimates:
##
## Estimate Std. Error t-value p-value
## d:(Intercept) 102.0337 9.5991 10.6295 0.0000
## e:(Intercept) 2.2813 1.7809 1.2810 0.2475
##
## Residual standard error:
##
## 21.75614 (6 degrees of freedom)
Results presentation and discussion for parent analytes (atrazine, triadimenon, and fipronil) and their xenobiotic metabolites.
Toxicity of parents and metabolites discussion.
Microsomal analysis of soil and amphibian data for 0 (soil only), 2, 4, 12, 24, and 48 hours after exposure.
Database has factors for time, parent (mapped to analyte), analyte (can be either parent or metabolite), matrix (amphibian or soil), and tank (potentially a nuisance variable).
## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame': 352 obs. of 6 variables:
## $ time : int 2 2 2 2 4 4 4 4 12 12 ...
## $ parent : Factor w/ 3 levels "atrazine","fipronil",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ analyte : Factor w/ 8 levels "atrazine","dea",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ matrix : Factor w/ 2 levels "amphib","soil": 1 1 1 1 1 1 1 1 1 1 ...
## $ conc : num 3.07 3.69 4.7 1.89 7.62 ...
## $ replicate: Factor w/ 4 levels "1","2","3","4": 1 2 3 4 1 2 3 4 1 2 ...
## - attr(*, "vars")=List of 4
## ..$ : symbol parent
## ..$ : symbol analyte
## ..$ : symbol matrix
## ..$ : symbol time
## - attr(*, "drop")= logi TRUE
## - attr(*, "indices")=List of 86
## ..$ : int 0 1 2 3
## ..$ : int 4 5 6 7
## ..$ : int 8 9 10 11
## ..$ : int 12 13 14 15
## ..$ : int 16 17 18 19
## ..$ : int 20 21 22 23
## ..$ : int 24 25 26 27
## ..$ : int 28 29 30 31
## ..$ : int 32 33 34 35
## ..$ : int 36 37 38 39
## ..$ : int 40 41 42 43
## ..$ : int 88 89 90 91
## ..$ : int 92 93 94 95
## ..$ : int 96 97 98 99
## ..$ : int 100 101 102 103
## ..$ : int 104 105 106 107
## ..$ : int 108 109 110 111
## ..$ : int 112 113 114 115
## ..$ : int 116 117 118 119
## ..$ : int 120 121 122 123
## ..$ : int 124 125 126 127
## ..$ : int 128 129 130 131
## ..$ : int 44 45 46 47
## ..$ : int 48 49 50 51
## ..$ : int 52 53 54 55
## ..$ : int 56 57 58 59
## ..$ : int 60 61 62 63
## ..$ : int 64 65 66 67
## ..$ : int 68 69 70 71
## ..$ : int 72 73 74 75
## ..$ : int 76 77 78 79
## ..$ : int 80 81 82 83
## ..$ : int 84 85 86 87
## ..$ : int 264 265 266 267
## ..$ : int 268 269 270 271
## ..$ : int 272 273 274 275
## ..$ : int 276 277 278 279
## ..$ : int 280 281 282 283
## ..$ : int 284 285 286 287 288 289 290 291
## ..$ : int 292 293 294 295
## ..$ : int 296 297 298 299
## ..$ : int 300 301 302 303
## ..$ : int 304 305 306 307
## ..$ : int 308 309 310 311
## ..$ : int 312 313 314 315
## ..$ : int 316 317 318 319
## ..$ : int 320 321 322 323
## ..$ : int 324 325 326 327
## ..$ : int 328 329 330 331 332 333 334 335
## ..$ : int 336 337 338 339
## ..$ : int 340 341 342 343
## ..$ : int 344 345 346 347
## ..$ : int 348 349 350 351
## ..$ : int 176 177 178 179
## ..$ : int 180 181 182 183
## ..$ : int 184 185 186 187
## ..$ : int 188 189 190 191
## ..$ : int 192 193 194 195
## ..$ : int 196 197 198 199
## ..$ : int 200 201 202 203
## ..$ : int 204 205 206 207
## ..$ : int 208 209 210 211
## ..$ : int 212 213 214 215
## ..$ : int 216 217 218 219
## ..$ : int 220 221 222 223
## ..$ : int 224 225 226 227
## ..$ : int 228 229 230 231
## ..$ : int 232 233 234 235
## ..$ : int 236 237 238 239
## ..$ : int 240 241 242 243
## ..$ : int 244 245 246 247
## ..$ : int 248 249 250 251
## ..$ : int 252 253 254 255
## ..$ : int 256 257 258 259
## ..$ : int 260 261 262 263
## ..$ : int 132 133 134 135
## ..$ : int 136 137 138 139
## ..$ : int 140 141 142 143
## ..$ : int 144 145 146 147
## ..$ : int 148 149 150 151
## ..$ : int 152 153 154 155
## ..$ : int 156 157 158 159
## ..$ : int 160 161 162 163
## ..$ : int 164 165 166 167
## ..$ : int 168 169 170 171
## ..$ : int 172 173 174 175
## - attr(*, "group_sizes")= int 4 4 4 4 4 4 4 4 4 4 ...
## - attr(*, "biggest_group_size")= int 8
## - attr(*, "labels")='data.frame': 86 obs. of 4 variables:
## ..$ parent : Factor w/ 3 levels "atrazine","fipronil",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..$ analyte: Factor w/ 8 levels "atrazine","dea",..: 1 1 1 1 1 1 1 1 1 1 ...
## ..$ matrix : Factor w/ 2 levels "amphib","soil": 1 1 1 1 1 2 2 2 2 2 ...
## ..$ time : int 2 4 12 24 48 0 2 4 12 24 ...
## ..- attr(*, "vars")=List of 4
## .. ..$ : symbol parent
## .. ..$ : symbol analyte
## .. ..$ : symbol matrix
## .. ..$ : symbol time
## ..- attr(*, "drop")= logi TRUE
## Source: local data frame [352 x 6]
## Groups: parent, analyte, matrix, time [86]
##
## time parent analyte matrix conc replicate
## (int) (fctr) (fctr) (fctr) (dbl) (fctr)
## 1 2 atrazine atrazine amphib 3.068203 1
## 2 2 atrazine atrazine amphib 3.689640 2
## 3 2 atrazine atrazine amphib 4.701166 3
## 4 2 atrazine atrazine amphib 1.892494 4
## 5 4 atrazine atrazine amphib 7.624610 1
## 6 4 atrazine atrazine amphib 4.134300 2
## 7 4 atrazine atrazine amphib 2.050779 3
## 8 4 atrazine atrazine amphib 7.626486 4
## 9 12 atrazine atrazine amphib 2.452278 1
## 10 12 atrazine atrazine amphib 3.786893 2
## .. ... ... ... ... ... ...
## Source: local data frame [86 x 7]
## Groups: parent, analyte, matrix [?]
##
## parent analyte matrix time count ConcMean ConcSD
## (fctr) (fctr) (fctr) (int) (int) (dbl) (dbl)
## 1 atrazine atrazine amphib 2 4 3.337876 1.1753225
## 2 atrazine atrazine amphib 4 4 5.359044 2.7518903
## 3 atrazine atrazine amphib 12 4 3.123811 1.2566289
## 4 atrazine atrazine amphib 24 4 1.554919 0.6096102
## 5 atrazine atrazine amphib 48 4 1.001887 0.3545473
## 6 atrazine atrazine soil 0 4 15.930920 2.5911041
## 7 atrazine atrazine soil 2 4 19.029034 4.3534303
## 8 atrazine atrazine soil 4 4 18.770091 2.6953158
## 9 atrazine atrazine soil 12 4 20.614190 3.8918511
## 10 atrazine atrazine soil 24 4 24.996196 4.4473828
## .. ... ... ... ... ... ... ...
## <!-- html table generated in R 3.2.3 by xtable 1.8-2 package -->
## <!-- Tue Mar 29 16:05:23 2016 -->
## <table border=1>
## <tr> <th> </th> <th> parent </th> <th> analyte </th> <th> matrix </th> <th> time </th> <th> count </th> <th> ConcMean </th> <th> ConcSD </th> </tr>
## <tr> <td align="right"> 1 </td> <td> atrazine </td> <td> atrazine </td> <td> amphib </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 3.34 </td> <td align="right"> 1.18 </td> </tr>
## <tr> <td align="right"> 2 </td> <td> atrazine </td> <td> atrazine </td> <td> amphib </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 5.36 </td> <td align="right"> 2.75 </td> </tr>
## <tr> <td align="right"> 3 </td> <td> atrazine </td> <td> atrazine </td> <td> amphib </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 3.12 </td> <td align="right"> 1.26 </td> </tr>
## <tr> <td align="right"> 4 </td> <td> atrazine </td> <td> atrazine </td> <td> amphib </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 1.55 </td> <td align="right"> 0.61 </td> </tr>
## <tr> <td align="right"> 5 </td> <td> atrazine </td> <td> atrazine </td> <td> amphib </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 1.00 </td> <td align="right"> 0.35 </td> </tr>
## <tr> <td align="right"> 6 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right"> 0 </td> <td align="right"> 4 </td> <td align="right"> 15.93 </td> <td align="right"> 2.59 </td> </tr>
## <tr> <td align="right"> 7 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 19.03 </td> <td align="right"> 4.35 </td> </tr>
## <tr> <td align="right"> 8 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 18.77 </td> <td align="right"> 2.70 </td> </tr>
## <tr> <td align="right"> 9 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 20.61 </td> <td align="right"> 3.89 </td> </tr>
## <tr> <td align="right"> 10 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 25.00 </td> <td align="right"> 4.45 </td> </tr>
## <tr> <td align="right"> 11 </td> <td> atrazine </td> <td> atrazine </td> <td> soil </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 26.43 </td> <td align="right"> 4.38 </td> </tr>
## <tr> <td align="right"> 12 </td> <td> atrazine </td> <td> dea </td> <td> amphib </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 13 </td> <td> atrazine </td> <td> dea </td> <td> amphib </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.03 </td> </tr>
## <tr> <td align="right"> 14 </td> <td> atrazine </td> <td> dea </td> <td> amphib </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.05 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 15 </td> <td> atrazine </td> <td> dea </td> <td> amphib </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 16 </td> <td> atrazine </td> <td> dea </td> <td> amphib </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.02 </td> </tr>
## <tr> <td align="right"> 17 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right"> 0 </td> <td align="right"> 4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 18 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 19 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 20 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 21 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 22 </td> <td> atrazine </td> <td> dea </td> <td> soil </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 23 </td> <td> atrazine </td> <td> dia </td> <td> amphib </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 0.33 </td> <td align="right"> 0.12 </td> </tr>
## <tr> <td align="right"> 24 </td> <td> atrazine </td> <td> dia </td> <td> amphib </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.94 </td> <td align="right"> 0.76 </td> </tr>
## <tr> <td align="right"> 25 </td> <td> atrazine </td> <td> dia </td> <td> amphib </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 1.08 </td> <td align="right"> 0.51 </td> </tr>
## <tr> <td align="right"> 26 </td> <td> atrazine </td> <td> dia </td> <td> amphib </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 1.99 </td> <td align="right"> 1.35 </td> </tr>
## <tr> <td align="right"> 27 </td> <td> atrazine </td> <td> dia </td> <td> amphib </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.66 </td> <td align="right"> 0.53 </td> </tr>
## <tr> <td align="right"> 28 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right"> 0 </td> <td align="right"> 4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 29 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> -0.01 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 30 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.02 </td> </tr>
## <tr> <td align="right"> 31 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.08 </td> <td align="right"> 0.07 </td> </tr>
## <tr> <td align="right"> 32 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.03 </td> </tr>
## <tr> <td align="right"> 33 </td> <td> atrazine </td> <td> dia </td> <td> soil </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.06 </td> <td align="right"> 0.06 </td> </tr>
## <tr> <td align="right"> 34 </td> <td> fipronil </td> <td> fipronil </td> <td> amphib </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 1.87 </td> <td align="right"> 1.35 </td> </tr>
## <tr> <td align="right"> 35 </td> <td> fipronil </td> <td> fipronil </td> <td> amphib </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 1.30 </td> <td align="right"> 0.52 </td> </tr>
## <tr> <td align="right"> 36 </td> <td> fipronil </td> <td> fipronil </td> <td> amphib </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.57 </td> <td align="right"> 0.24 </td> </tr>
## <tr> <td align="right"> 37 </td> <td> fipronil </td> <td> fipronil </td> <td> amphib </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.27 </td> <td align="right"> 0.05 </td> </tr>
## <tr> <td align="right"> 38 </td> <td> fipronil </td> <td> fipronil </td> <td> amphib </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.21 </td> <td align="right"> 0.13 </td> </tr>
## <tr> <td align="right"> 39 </td> <td> fipronil </td> <td> fipronil </td> <td> soil </td> <td align="right"> 2 </td> <td align="right"> 8 </td> <td align="right"> 0.41 </td> <td align="right"> 0.45 </td> </tr>
## <tr> <td align="right"> 40 </td> <td> fipronil </td> <td> fipronil </td> <td> soil </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.87 </td> <td align="right"> 0.24 </td> </tr>
## <tr> <td align="right"> 41 </td> <td> fipronil </td> <td> fipronil </td> <td> soil </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.82 </td> <td align="right"> 0.17 </td> </tr>
## <tr> <td align="right"> 42 </td> <td> fipronil </td> <td> fipronil </td> <td> soil </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.70 </td> <td align="right"> 0.11 </td> </tr>
## <tr> <td align="right"> 43 </td> <td> fipronil </td> <td> fipronil </td> <td> soil </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.36 </td> <td align="right"> 0.06 </td> </tr>
## <tr> <td align="right"> 44 </td> <td> fipronil </td> <td> fipsulf </td> <td> amphib </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 0.35 </td> <td align="right"> 0.29 </td> </tr>
## <tr> <td align="right"> 45 </td> <td> fipronil </td> <td> fipsulf </td> <td> amphib </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.40 </td> <td align="right"> 0.48 </td> </tr>
## <tr> <td align="right"> 46 </td> <td> fipronil </td> <td> fipsulf </td> <td> amphib </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.61 </td> <td align="right"> 0.57 </td> </tr>
## <tr> <td align="right"> 47 </td> <td> fipronil </td> <td> fipsulf </td> <td> amphib </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 1.25 </td> <td align="right"> 0.53 </td> </tr>
## <tr> <td align="right"> 48 </td> <td> fipronil </td> <td> fipsulf </td> <td> amphib </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.91 </td> <td align="right"> 0.57 </td> </tr>
## <tr> <td align="right"> 49 </td> <td> fipronil </td> <td> fipsulf </td> <td> soil </td> <td align="right"> 2 </td> <td align="right"> 8 </td> <td align="right"> 0.01 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 50 </td> <td> fipronil </td> <td> fipsulf </td> <td> soil </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 51 </td> <td> fipronil </td> <td> fipsulf </td> <td> soil </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 52 </td> <td> fipronil </td> <td> fipsulf </td> <td> soil </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 53 </td> <td> fipronil </td> <td> fipsulf </td> <td> soil </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 54 </td> <td> triadimefon </td> <td> tdla </td> <td> amphib </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 0.06 </td> <td align="right"> 0.02 </td> </tr>
## <tr> <td align="right"> 55 </td> <td> triadimefon </td> <td> tdla </td> <td> amphib </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.08 </td> <td align="right"> 0.05 </td> </tr>
## <tr> <td align="right"> 56 </td> <td> triadimefon </td> <td> tdla </td> <td> amphib </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.02 </td> </tr>
## <tr> <td align="right"> 57 </td> <td> triadimefon </td> <td> tdla </td> <td> amphib </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 58 </td> <td> triadimefon </td> <td> tdla </td> <td> amphib </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 59 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right"> 0 </td> <td align="right"> 4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 60 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 61 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 62 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 63 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 64 </td> <td> triadimefon </td> <td> tdla </td> <td> soil </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 65 </td> <td> triadimefon </td> <td> tdlb </td> <td> amphib </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 0.08 </td> <td align="right"> 0.02 </td> </tr>
## <tr> <td align="right"> 66 </td> <td> triadimefon </td> <td> tdlb </td> <td> amphib </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.13 </td> <td align="right"> 0.05 </td> </tr>
## <tr> <td align="right"> 67 </td> <td> triadimefon </td> <td> tdlb </td> <td> amphib </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.08 </td> <td align="right"> 0.05 </td> </tr>
## <tr> <td align="right"> 68 </td> <td> triadimefon </td> <td> tdlb </td> <td> amphib </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 69 </td> <td> triadimefon </td> <td> tdlb </td> <td> amphib </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.03 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 70 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right"> 0 </td> <td align="right"> 4 </td> <td align="right"> 0.00 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 71 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 72 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.01 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 73 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.00 </td> </tr>
## <tr> <td align="right"> 74 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.02 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 75 </td> <td> triadimefon </td> <td> tdlb </td> <td> soil </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.04 </td> <td align="right"> 0.01 </td> </tr>
## <tr> <td align="right"> 76 </td> <td> triadimefon </td> <td> triadimefon </td> <td> amphib </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 0.39 </td> <td align="right"> 0.08 </td> </tr>
## <tr> <td align="right"> 77 </td> <td> triadimefon </td> <td> triadimefon </td> <td> amphib </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 0.55 </td> <td align="right"> 0.30 </td> </tr>
## <tr> <td align="right"> 78 </td> <td> triadimefon </td> <td> triadimefon </td> <td> amphib </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 0.75 </td> <td align="right"> 0.32 </td> </tr>
## <tr> <td align="right"> 79 </td> <td> triadimefon </td> <td> triadimefon </td> <td> amphib </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 0.87 </td> <td align="right"> 0.86 </td> </tr>
## <tr> <td align="right"> 80 </td> <td> triadimefon </td> <td> triadimefon </td> <td> amphib </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 0.25 </td> <td align="right"> 0.08 </td> </tr>
## <tr> <td align="right"> 81 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right"> 0 </td> <td align="right"> 4 </td> <td align="right"> 3.11 </td> <td align="right"> 0.85 </td> </tr>
## <tr> <td align="right"> 82 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right"> 2 </td> <td align="right"> 4 </td> <td align="right"> 3.66 </td> <td align="right"> 1.34 </td> </tr>
## <tr> <td align="right"> 83 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right"> 4 </td> <td align="right"> 4 </td> <td align="right"> 5.28 </td> <td align="right"> 0.37 </td> </tr>
## <tr> <td align="right"> 84 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right"> 12 </td> <td align="right"> 4 </td> <td align="right"> 5.93 </td> <td align="right"> 1.00 </td> </tr>
## <tr> <td align="right"> 85 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right"> 24 </td> <td align="right"> 4 </td> <td align="right"> 4.82 </td> <td align="right"> 0.32 </td> </tr>
## <tr> <td align="right"> 86 </td> <td> triadimefon </td> <td> triadimefon </td> <td> soil </td> <td align="right"> 48 </td> <td align="right"> 4 </td> <td align="right"> 4.18 </td> <td align="right"> 0.44 </td> </tr>
## </table>
The amphibian data set summary statistics. Atrazine peaks at 4 hours and then declines monotonically. DEA peaks at 12 hours and DIA at 24. Fipronil steadily declines from its first observation at 2 hours with fipronil sulfone not peaking until 24 hours. Triadimenon peaks at 24 hours with its metabolites tdla and tdlb peaking at 4 hours.
## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame': 86 obs. of 7 variables:
## $ parent : Factor w/ 3 levels "atrazine","fipronil",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ analyte : Factor w/ 8 levels "atrazine","dea",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ matrix : Factor w/ 2 levels "amphib","soil": 1 1 1 1 1 2 2 2 2 2 ...
## $ time : int 2 4 12 24 48 0 2 4 12 24 ...
## $ count : int 4 4 4 4 4 4 4 4 4 4 ...
## $ ConcMean: num 3.34 5.36 3.12 1.55 1 ...
## $ ConcSD : num 1.175 2.752 1.257 0.61 0.355 ...
## - attr(*, "vars")=List of 3
## ..$ : symbol parent
## ..$ : symbol analyte
## ..$ : symbol matrix
## - attr(*, "drop")= logi TRUE
## Source: local data frame [6 x 7]
## Groups: parent, analyte, matrix [2]
##
## parent analyte matrix time count ConcMean ConcSD
## (fctr) (fctr) (fctr) (int) (int) (dbl) (dbl)
## 1 atrazine atrazine amphib 2 4 3.33787563 1.17532253
## 2 atrazine atrazine amphib 4 4 5.35904387 2.75189034
## 3 atrazine atrazine amphib 12 4 3.12381130 1.25662887
## 4 atrazine atrazine amphib 24 4 1.55491895 0.60961016
## 5 atrazine atrazine amphib 48 4 1.00188749 0.35454734
## 6 atrazine dea amphib 2 4 0.01389877 0.00522407
## Source: local data frame [15 x 7]
## Groups: parent, analyte, matrix [3]
##
## parent analyte matrix time count ConcMean ConcSD
## (fctr) (fctr) (fctr) (int) (int) (dbl) (dbl)
## 1 atrazine atrazine amphib 2 4 3.33787563 1.17532253
## 2 atrazine atrazine amphib 4 4 5.35904387 2.75189034
## 3 atrazine atrazine amphib 12 4 3.12381130 1.25662887
## 4 atrazine atrazine amphib 24 4 1.55491895 0.60961016
## 5 atrazine atrazine amphib 48 4 1.00188749 0.35454734
## 6 atrazine dea amphib 2 4 0.01389877 0.00522407
## 7 atrazine dea amphib 4 4 0.04436711 0.02843181
## 8 atrazine dea amphib 12 4 0.05402182 0.01282235
## 9 atrazine dea amphib 24 4 0.03652473 0.01310278
## 10 atrazine dea amphib 48 4 0.01951501 0.01958540
## 11 atrazine dia amphib 2 4 0.32878185 0.12495746
## 12 atrazine dia amphib 4 4 0.94192818 0.76074427
## 13 atrazine dia amphib 12 4 1.08350960 0.50576827
## 14 atrazine dia amphib 24 4 1.98835970 1.34925248
## 15 atrazine dia amphib 48 4 0.66238821 0.53051448
## [1] "atrazine"
## [1] "dea"
## [1] "dia"
## Source: local data frame [10 x 7]
## Groups: parent, analyte, matrix [2]
##
## parent analyte matrix time count ConcMean ConcSD
## (fctr) (fctr) (fctr) (int) (int) (dbl) (dbl)
## 1 fipronil fipronil amphib 2 4 1.8670398 1.34631230
## 2 fipronil fipronil amphib 4 4 1.3005836 0.52188454
## 3 fipronil fipronil amphib 12 4 0.5659565 0.24026633
## 4 fipronil fipronil amphib 24 4 0.2676732 0.05106111
## 5 fipronil fipronil amphib 48 4 0.2119504 0.13140536
## 6 fipronil fipsulf amphib 2 4 0.3465734 0.28711156
## 7 fipronil fipsulf amphib 4 4 0.3966089 0.47739157
## 8 fipronil fipsulf amphib 12 4 0.6129114 0.57346517
## 9 fipronil fipsulf amphib 24 4 1.2528179 0.52648677
## 10 fipronil fipsulf amphib 48 4 0.9067126 0.56596417
## Source: local data frame [0 x 7]
## Groups: parent, analyte, matrix [0]
##
## Variables not shown: parent (fctr), analyte (fctr), matrix (fctr), time
## (int), count (int), ConcMean (dbl), ConcSD (dbl)
The soil data set summary statistics. Atrazine in soil showing an upwards trend (need to test if significant) with dia and dea levels very low indicating little degradation in soil over the 48 hour period. Fipronil and triadimenon data also indicating little degradation in soil.
## Source: local data frame [18 x 7]
## Groups: parent, analyte, matrix [3]
##
## parent analyte matrix time count ConcMean ConcSD
## (fctr) (fctr) (fctr) (int) (int) (dbl) (dbl)
## 1 atrazine atrazine soil 0 4 15.930919692 2.591104068
## 2 atrazine atrazine soil 2 4 19.029034412 4.353430337
## 3 atrazine atrazine soil 4 4 18.770091080 2.695315758
## 4 atrazine atrazine soil 12 4 20.614190363 3.891851074
## 5 atrazine atrazine soil 24 4 24.996195532 4.447382801
## 6 atrazine atrazine soil 48 4 26.434744900 4.377099498
## 7 atrazine dea soil 0 4 0.000000000 0.000000000
## 8 atrazine dea soil 2 4 0.001570429 0.000633009
## 9 atrazine dea soil 4 4 0.002999216 0.001537652
## 10 atrazine dea soil 12 4 0.005469015 0.004234880
## 11 atrazine dea soil 24 4 0.007308439 0.001736211
## 12 atrazine dea soil 48 4 0.008784632 0.003772339
## 13 atrazine dia soil 0 4 0.000000000 0.000000000
## 14 atrazine dia soil 2 4 -0.007727286 0.007374085
## 15 atrazine dia soil 4 4 0.020538600 0.020166308
## 16 atrazine dia soil 12 4 0.077036921 0.067772652
## 17 atrazine dia soil 24 4 0.042975804 0.030007793
## 18 atrazine dia soil 48 4 0.063693292 0.058241606
## Source: local data frame [10 x 7]
## Groups: parent, analyte, matrix [2]
##
## parent analyte matrix time count ConcMean ConcSD
## (fctr) (fctr) (fctr) (int) (int) (dbl) (dbl)
## 1 fipronil fipronil soil 2 8 0.40768123 0.447265792
## 2 fipronil fipronil soil 4 4 0.86826316 0.244191727
## 3 fipronil fipronil soil 12 4 0.81738069 0.169691335
## 4 fipronil fipronil soil 24 4 0.70225877 0.108745058
## 5 fipronil fipronil soil 48 4 0.35560948 0.058543752
## 6 fipronil fipsulf soil 2 8 0.01022479 0.012898421
## 7 fipronil fipsulf soil 4 4 0.02193831 0.010765665
## 8 fipronil fipsulf soil 12 4 0.01829893 0.005013814
## 9 fipronil fipsulf soil 24 4 0.01917848 0.009139211
## 10 fipronil fipsulf soil 48 4 0.01262887 0.005517408
## Source: local data frame [0 x 7]
## Groups: parent, analyte, matrix [0]
##
## Variables not shown: parent (fctr), analyte (fctr), matrix (fctr), time
## (int), count (int), ConcMean (dbl), ConcSD (dbl)
pdf(paste(micro.graphics,"data_mean_scatterplot",".pdf", sep=""))
par(mfrow=c(3,1))
print(parents)
## [1] atrazine triadimefon fipronil
## Levels: atrazine fipronil triadimefon
for(parent in parents){
i=0
print(parent)
temp.parent <- micro.group.stats.amphib[which(micro.group.stats.amphib$parent==parent),]
print(temp.parent)
parent.analytes <- unique(temp.parent$analyte)
for(analyte in parent.analytes){
print(parent)
print(analyte)
analytetemp <- micro.group.stats.amphib[which(micro.group.stats.amphib$analyte==analyte),]
xvalues <- as.numeric(as.character(analytetemp$time))
points.y <- micro.amphib[which(micro.amphib$analyte==analyte),]$conc
points.x <- as.numeric(as.character(micro.amphib[which(micro.amphib$analyte==analyte),]$time))
#create empty plot if needed
if(i==0){
parenttemp <- micro.group.stats.amphib[which(micro.group.stats.amphib$parent==parent),]
maxconc <- max(points.y)
plot(xvalues,analytetemp$ConcMean,type="l", xlim=c(0,48),
ylim = c(0,maxconc), main=parent,col="black", xlab="Hours", ylab="Concentration")
axis(1,at=xvalues)
i=1
}
#add line for parent metabolite
if(analyte %in% parents){
lines(xvalues,analytetemp$ConcMean,type="l",col="red")
points(points.x, points.y, col = "red")
}else{
#add line for daughter metabolite
lines(xvalues,analytetemp$ConcMean,type="l",col="blue")
points(points.x, points.y, col = "blue")
}
}
}
## [1] "atrazine"
## Source: local data frame [15 x 7]
## Groups: parent, analyte, matrix [3]
##
## parent analyte matrix time count ConcMean ConcSD
## (fctr) (fctr) (fctr) (int) (int) (dbl) (dbl)
## 1 atrazine atrazine amphib 2 4 3.33787563 1.17532253
## 2 atrazine atrazine amphib 4 4 5.35904387 2.75189034
## 3 atrazine atrazine amphib 12 4 3.12381130 1.25662887
## 4 atrazine atrazine amphib 24 4 1.55491895 0.60961016
## 5 atrazine atrazine amphib 48 4 1.00188749 0.35454734
## 6 atrazine dea amphib 2 4 0.01389877 0.00522407
## 7 atrazine dea amphib 4 4 0.04436711 0.02843181
## 8 atrazine dea amphib 12 4 0.05402182 0.01282235
## 9 atrazine dea amphib 24 4 0.03652473 0.01310278
## 10 atrazine dea amphib 48 4 0.01951501 0.01958540
## 11 atrazine dia amphib 2 4 0.32878185 0.12495746
## 12 atrazine dia amphib 4 4 0.94192818 0.76074427
## 13 atrazine dia amphib 12 4 1.08350960 0.50576827
## 14 atrazine dia amphib 24 4 1.98835970 1.34925248
## 15 atrazine dia amphib 48 4 0.66238821 0.53051448
## [1] "atrazine"
## [1] "atrazine"
## [1] "atrazine"
## [1] "dea"
## [1] "atrazine"
## [1] "dia"
## [1] "triadimefon"
## Source: local data frame [15 x 7]
## Groups: parent, analyte, matrix [3]
##
## parent analyte matrix time count ConcMean ConcSD
## (fctr) (fctr) (fctr) (int) (int) (dbl) (dbl)
## 1 triadimefon tdla amphib 2 4 0.05514470 0.021089764
## 2 triadimefon tdla amphib 4 4 0.08150175 0.046221502
## 3 triadimefon tdla amphib 12 4 0.03632384 0.015222899
## 4 triadimefon tdla amphib 24 4 0.02400790 0.007522554
## 5 triadimefon tdla amphib 48 4 0.01549908 0.004845715
## 6 triadimefon tdlb amphib 2 4 0.08362714 0.016364595
## 7 triadimefon tdlb amphib 4 4 0.13009543 0.054315693
## 8 triadimefon tdlb amphib 12 4 0.07664673 0.048553355
## 9 triadimefon tdlb amphib 24 4 0.04183235 0.011652266
## 10 triadimefon tdlb amphib 48 4 0.02950214 0.003438978
## 11 triadimefon triadimefon amphib 2 4 0.38678831 0.075852995
## 12 triadimefon triadimefon amphib 4 4 0.54862458 0.300394386
## 13 triadimefon triadimefon amphib 12 4 0.75491439 0.318237965
## 14 triadimefon triadimefon amphib 24 4 0.86540039 0.860421208
## 15 triadimefon triadimefon amphib 48 4 0.24868748 0.079848623
## [1] "triadimefon"
## [1] "tdla"
## [1] "triadimefon"
## [1] "tdlb"
## [1] "triadimefon"
## [1] "triadimefon"
## [1] "fipronil"
## Source: local data frame [10 x 7]
## Groups: parent, analyte, matrix [2]
##
## parent analyte matrix time count ConcMean ConcSD
## (fctr) (fctr) (fctr) (int) (int) (dbl) (dbl)
## 1 fipronil fipronil amphib 2 4 1.8670398 1.34631230
## 2 fipronil fipronil amphib 4 4 1.3005836 0.52188454
## 3 fipronil fipronil amphib 12 4 0.5659565 0.24026633
## 4 fipronil fipronil amphib 24 4 0.2676732 0.05106111
## 5 fipronil fipronil amphib 48 4 0.2119504 0.13140536
## 6 fipronil fipsulf amphib 2 4 0.3465734 0.28711156
## 7 fipronil fipsulf amphib 4 4 0.3966089 0.47739157
## 8 fipronil fipsulf amphib 12 4 0.6129114 0.57346517
## 9 fipronil fipsulf amphib 24 4 1.2528179 0.52648677
## 10 fipronil fipsulf amphib 48 4 0.9067126 0.56596417
## [1] "fipronil"
## [1] "fipronil"
## [1] "fipronil"
## [1] "fipsulf"
dev.off()
## quartz_off_screen
## 2
la de da
library(maxLik)
MLexp <- function(times, data){
expLik <- function(param) { y <- data t <- times alpha1 <- param[1] lambda1 <- param[2]-1(alpha1-lambda1t) - y/(exp(alpha1-lambda1*t)) }
max.fit <- maxLik(expLik, start = c(1,1)) crit <- qt(.975, length(data)-2) max.intercept.CI <- c(summary(max.fit)\(est[1,1]-crit*summary(max.fit)\)est[1,2],summary(max.fit)\(est[1,1]+crit*summary(max.fit)\)est[1,2]) max.decayrate.CI <- c(-1summary(max.fit)\(est[2,1]-crit*summary(max.fit)\)est[2,2],-1summary(max.fit)\(est[2,1]+crit*summary(max.fit)\)est[2,2]) max.halflife.CI <- c(log(2)/max.decayrate.CI[2],log(2)/max.decayrate.CI[1])
paste(“MLE for Initial Concentration =”,max.fit\(est[1], "MLE for decay rate =", max.fit\)est[2], “MLE for half life =”, -log(.5)/max.fit$est[2])
temp.list <- list(max.fit\(est[1], max.fit\)est[2], -log(.5)/max.fit$est[2], max.intercept.CI, max.decayrate.CI, max.halflife.CI, vcov(max.fit) ) return(temp.list) }
MLexp.intercept <- function(times, data) { temp.fit <- MLexp(times,data) temp.fit[[1]] }
MLexp.decayrate <- function(times, data) { temp.fit <- MLexp(times,data) temp.fit[[2]] }
MLexp.halflife <- function(times,data) { temp.fit <- MLexp(times,data) temp.fit[[3]] }
MLexp.intercept.CI <- function(times,data){ temp.fit <- MLexp(times,data) temp2 <- temp.fit[[4]] names(temp2) <- c(“Lower95”, “Upper95”) temp2 }
MLexp.decayrate.CI <- function(times,data){ temp.fit <- MLexp(times,data) temp2 <- temp.fit[[5]] names(temp2) <- c(“Lower95”, “Upper95”) temp2 }
MLexp.halflife.CI <- function(times,data){ temp.fit <- MLexp(times,data) temp2 <- temp.fit[[6]] names(temp2) <- c(“Lower95”, “Upper95”) temp2 }
data in csv format